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How to analyse data after applying pandas' groupby function?
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Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
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Data science time! April 2019 and salary with experienceHow to flush output of print function?How to make a chain of function decorators?Converting a Pandas GroupBy object to DataFrame“Large data” work flows using pandasChange data type of columns in PandasHow do I get the row count of a pandas DataFrame?How to iterate over rows in a DataFrame in Pandas?Python How to find average of columns using dataframes apply methodPandas groupby().apply() - returning None from the applied function messes up the resultsShaping the input for LSTM model using Keras Python
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I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?
Here is my data frame.
ID Name Sex Age City Sport Medal
0 1 A Dijiang M 24.0 Barcelona Basketball Gold
1 2 A Lamusi M 23.0 London Judo Silver
...
I applied the following function to my data frame called qq:
zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()
NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2
After applying the function how can I analyse this zz series?
For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?
python dataset pandas
asked Mar 31 at 2:59
a_a_aa_a_a
968
migrated from datascience.stackexchange.com Mar 31 at 16:35
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?
Here is my data frame.
ID Name Sex Age City Sport Medal
0 1 A Dijiang M 24.0 Barcelona Basketball Gold
1 2 A Lamusi M 23.0 London Judo Silver
...
I applied the following function to my data frame called qq:
zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()
NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2
After applying the function how can I analyse this zz series?
For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?
python dataset pandas
asked Mar 31 at 2:59
a_a_aa_a_a
968
migrated from datascience.stackexchange.com Mar 31 at 16:35
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
Does each and every record have a medal? In that case remove theqq.Medal =='Gold'and'Medal'.
– Esmailian
Mar 31 at 9:01
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
what are the possible values inMedalcolumn?
– Esmailian
Mar 31 at 20:33
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05
add a comment |
I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?
Here is my data frame.
ID Name Sex Age City Sport Medal
0 1 A Dijiang M 24.0 Barcelona Basketball Gold
1 2 A Lamusi M 23.0 London Judo Silver
...
I applied the following function to my data frame called qq:
zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()
NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2
After applying the function how can I analyse this zz series?
For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?
python dataset pandas
asked Mar 31 at 2:59
a_a_aa_a_a
968
I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?
Here is my data frame.
ID Name Sex Age City Sport Medal
0 1 A Dijiang M 24.0 Barcelona Basketball Gold
1 2 A Lamusi M 23.0 London Judo Silver
...
I applied the following function to my data frame called qq:
zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()
NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2
After applying the function how can I analyse this zz series?
For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?
python dataset pandas
python dataset pandas
asked Mar 31 at 2:59
a_a_aa_a_a
968
asked Mar 31 at 2:59
a_a_aa_a_a
968
asked Mar 31 at 2:59
a_a_aa_a_a
968
asked Mar 31 at 2:59
a_a_aa_a_a
968
asked Mar 31 at 2:59
a_a_aa_a_a
968
968
migrated from datascience.stackexchange.com Mar 31 at 16:35
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
migrated from datascience.stackexchange.com Mar 31 at 16:35
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
Does each and every record have a medal? In that case remove theqq.Medal =='Gold'and'Medal'.
– Esmailian
Mar 31 at 9:01
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
what are the possible values inMedalcolumn?
– Esmailian
Mar 31 at 20:33
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05
add a comment |
Does each and every record have a medal? In that case remove theqq.Medal =='Gold'and'Medal'.
– Esmailian
Mar 31 at 9:01
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
what are the possible values inMedalcolumn?
– Esmailian
Mar 31 at 20:33
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05
Does each and every record have a medal? In that case remove the
qq.Medal =='Gold' and 'Medal'.– Esmailian
Mar 31 at 9:01
Does each and every record have a medal? In that case remove the
qq.Medal =='Gold' and 'Medal'.– Esmailian
Mar 31 at 9:01
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
what are the possible values in
Medal column?– Esmailian
Mar 31 at 20:33
what are the possible values in
Medal column?– Esmailian
Mar 31 at 20:33
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05
add a comment |
1 Answer
1
active
oldest
votes
You need to first filter out the NaN medals, and then aggregate. Here is an example:
import pandas as pd
df = pd.DataFrame([['USA', 'Gold'],
['USA', 'Bronze'],
['USA', 'NaN'],
['UK', 'Silver'],
['UK', 'NaN']],
columns=['NOC', 'Medal'])
valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
.count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])
Output:
NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object
answered Apr 1 at 13:12
EsmailianEsmailian
12112
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You need to first filter out the NaN medals, and then aggregate. Here is an example:
import pandas as pd
df = pd.DataFrame([['USA', 'Gold'],
['USA', 'Bronze'],
['USA', 'NaN'],
['UK', 'Silver'],
['UK', 'NaN']],
columns=['NOC', 'Medal'])
valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
.count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])
Output:
NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object
answered Apr 1 at 13:12
EsmailianEsmailian
12112
add a comment |
You need to first filter out the NaN medals, and then aggregate. Here is an example:
import pandas as pd
df = pd.DataFrame([['USA', 'Gold'],
['USA', 'Bronze'],
['USA', 'NaN'],
['UK', 'Silver'],
['UK', 'NaN']],
columns=['NOC', 'Medal'])
valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
.count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])
Output:
NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object
answered Apr 1 at 13:12
EsmailianEsmailian
12112
add a comment |
You need to first filter out the NaN medals, and then aggregate. Here is an example:
import pandas as pd
df = pd.DataFrame([['USA', 'Gold'],
['USA', 'Bronze'],
['USA', 'NaN'],
['UK', 'Silver'],
['UK', 'NaN']],
columns=['NOC', 'Medal'])
valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
.count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])
Output:
NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object
answered Apr 1 at 13:12
EsmailianEsmailian
12112
You need to first filter out the NaN medals, and then aggregate. Here is an example:
import pandas as pd
df = pd.DataFrame([['USA', 'Gold'],
['USA', 'Bronze'],
['USA', 'NaN'],
['UK', 'Silver'],
['UK', 'NaN']],
columns=['NOC', 'Medal'])
valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
.count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])
Output:
NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object
answered Apr 1 at 13:12
EsmailianEsmailian
12112
answered Apr 1 at 13:12
EsmailianEsmailian
12112
answered Apr 1 at 13:12
EsmailianEsmailian
12112
answered Apr 1 at 13:12
EsmailianEsmailian
12112
12112
add a comment |
add a comment |
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Does each and every record have a medal? In that case remove the
qq.Medal =='Gold'and'Medal'.– Esmailian
Mar 31 at 9:01
No. Not every country won a medal.
– a_a_a
Mar 31 at 20:24
what are the possible values in
Medalcolumn?– Esmailian
Mar 31 at 20:33
Gold Silver Bronze NaN
– a_a_a
Apr 1 at 5:05